1. Technical Field
The present disclosure relates to the interpolation or measurement of Head Related Transfer Functions (HRTFs). More particularly, the present disclosure relates to specific methods to the analysis of HRTF data from collections of measured or computed data of HRTFs.
2. Background of Related Art
The human ability to perceive the direction of a sound source is partly the result of cues encoded in the sound reaching the eardrum after scattering off of the listener's anatomic features (torso, head, and outer ears). The frequency response of how sound is modified in phase and magnitude by such scattering is called the Head-Related Transfer Function (HRTF) and is specific to each person. Knowledge of the HRTF allows for the reconstruction of realistic auditory scenes.
While the ability to measure and compute HRTFs has existed for several years, and HRTFs of human subjects have been collected by different labs, there remain several issues with their widespread use. First, HRTFs show considerable variability between individuals. Second, each measurement facility seems to use an individual process to obtain the HRTF using varying excitation signals, sampling frequencies, and more importantly measurement grids. The latter is a larger problem than may be initially thought, as the measurement grids are neither spatially uniform nor high resolution; time/cost issues and peculiarities of each measurement apparatus are limiting factors. FIG. 1 illustrates a typical HRTF measurement grid. To overcome the grid problem, solutions via spherical interpolation techniques are either performed on a per-frequency basis or in a principal component weight space over the measurement grid per subject. Yet another problem is that often measured HRTFs for a subject are not available, and the HRTFs need to be personalized to the subject. Personalization in a tensor-product principal component space has been attempted.
A key development in statistical modeling has been the development of Bayesian methods, which learn from available data, and allow the incorporation of informative prior models. If HRTFs can be jointly modeled in their spatial-frequency domain under a Bayesian setting, then it might be possible to improve the ability to deal with these issues. Moreover, such a modeling can be done in an informative feature space, as is often done in speech-processing and image-processing. Spectral features (such as peaks and notches) are promising and correlate listening cues along specific directions (median plane) to anatomical features.